Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical Systems using Constrained Autoencoders

Recently developed reduced-order modeling techniques aim to approximate nonlinear dynamical systems on low-dimensional manifolds learned from data. This is an effective approach for modeling dynamics in a post-transient regime where the effects of initial conditions and other disturbances have decay...

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Bibliographische Detailangaben

Hauptverfasser:	Otto, Samuel E, Macchio, Gregory R, Rowley, Clarence W
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computational Engineering, Finance, and Science Computer Science - Learning Computer Science - Systems and Control Mathematics - Differential Geometry Mathematics - Dynamical Systems
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